TensorFlow 学习笔记(一)

1、name_scope(name)返回一个语义管理器，调用

with name_scope(...):

将这个scope下的变量放在对应的scope的语义环境下
1）如果name是不以/结尾的，那么该函数就会创建一个新的scope出来，这个name会append在之前的一些创建的operation之上，例如下面代码的第九行。一旦name在之前出现过，将会重新命名，例如第十九行的inner_1
2) with g.name_scope(…) as scope:将会让代码重新回到一个绝对的scope中，脱离之前的继承性的scope，例如倒数第六行
3）一个None或者空string都会将目前的scope调整到最高级别，倒数第二行代码

with tf.Graph().as_default() as g:
  c = tf.constant(5.0, name="c")
  assert c.op.name == "c"
  c_1 = tf.constant(6.0, name="c")
  assert c_1.op.name == "c_1"

  # Creates a scope called "nested"
  with g.name_scope("nested") as scope:
    nested_c = tf.constant(10.0, name="c")
    assert nested_c.op.name == "nested/c"

    # Creates a nested scope called "inner".
    with g.name_scope("inner"):
      nested_inner_c = tf.constant(20.0, name="c")
      assert nested_inner_c.op.name == "nested/inner/c"

    # Create a nested scope called "inner_1".
    with g.name_scope("inner"):
      nested_inner_1_c = tf.constant(30.0, name="c")
      assert nested_inner_1_c.op.name == "nested/inner_1/c"

      # Treats `scope` as an absolute name scope, and
      # switches to the "nested/" scope.
      with g.name_scope(scope):
        nested_d = tf.constant(40.0, name="d")
        assert nested_d.op.name == "nested/d"

        with g.name_scope(""):
          e = tf.constant(50.0, name="e")
          assert e.op.name == "e"

2、slim.model_variable，slim.variable
在tf中，创建一个变量，需要对他进行初始化，并且如果指定所在的gpu，那么需要给出明确的指令，with tf.device(‘/cpu:0’):，但是slim的variable创建就可以一键完成

weights = slim.variable('weights',
                             shape=[10, 10, 3 , 3],
                             initializer=tf.truncated_normal_initializer(stddev=0.1),
                             regularizer=slim.l2_regularizer(0.05),
                             device='/CPU:0')

此外，在原始tf中，存在regular variables and local (transient) variables，regular variables可以被保存到disk上，但是 local (transient) variables却不可以，在slim中定义了model_variable，代表模型的参数，在训练过或者微调中使用到他或者在测试的时候从checkpoint加载他，slim.model_variable或者slim.fully_connected or slim.conv2d创建的变量都是model variable，但是如果有一些变量是其他方式构建的，但我们仍然希望他是model variable，需要怎么做呢？

# Letting TF-Slim know about the additional variable.
slim.add_model_variable(my_model_variable)

3、tf.variable_scope,tf.get_variable_scope
variable_scope是一个语义管理器用来定义创建变量的ops,

with tf.variable_scope("foo"):
    with tf.variable_scope("bar"):
        v = tf.get_variable("v", [1])
        assert v.name == "foo/bar/v:0"

变量重用，共享变量

def foo():
  with tf.variable_scope("foo", reuse=tf.AUTO_REUSE):
    v = tf.get_variable("v", [1])
  return v

v1 = foo()  # Creates v.
v2 = foo()  # Gets the same, existing v.
assert v1 == v2

with tf.variable_scope("foo") as scope:
    v = tf.get_variable("v", [1])
    scope.reuse_variables()
    v1 = tf.get_variable("v", [1])
assert v1 == v

如果不指定reuse=True就会报错，原因是不能创建同一个变量两次，如果制定了reuse=True，表示可以二者共享变量

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True):
    v1 = tf.get_variable("v", [1])
assert v1 == v

如果不共享的话，那么

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])   #foo/v
    with tf.variable_scope("foo"):
        v1 = tf.get_variable("v", [1])  #foo/foo/v

这样是可以的
对于共享变量而言，name_scope对于共享是没有影响的，get_variable是共享的，其他方式建立的变量不共享

with tf.variable_scope(tf.get_variable_scope()):
    for ii in range(5):
        with tf.name_scope('tower_%d' % ii) as name_scope:
            a = tf.get_variable('a',shape=[2])
            b = tf.Variable(1,name='b')
            c = tf.multiply(a,[1])
            print(a.name)
            print(b.name)
            print(c.name)
            tf.get_variable_scope().reuse_variables()

这里写图片描述
4、
tf.name_scope和tf.variable_scope嵌套使用

import tensorflow as tf
with tf.name_scope("name1"):
    with tf.variable_scope("var1"):
        a = tf.get_variable("a",shape=[2])
        b = tf.multiply(a,[1])
        c = tf.Variable(2,name="c")
print(a.name)
print(b.name)
print(c.name)

with tf.variable_scope("var2"):
    with tf.name_scope("name2"):
        a = tf.get_variable("a",shape=[2])
        b = tf.multiply(a,[1])
        c = tf.Variable(1,name="c")

print(a.name)
print(b.name)
print(c.name)

这里写图片描述
1) 嵌套顺序对结果无影响
2) 对于tf.Variable，name_scope和variable_scope都对她的命名产生影响
3）tf.get_variable()，name_scope不对他产生影响
4）对于一般的op操作，例如mulitiply，add等操作，name_scope和variable_scope都会对结果产生影响。

TensorFlow 学习笔记(一)

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